PostgreSQL has a rich set of
native data types available to users. Users may add new types to
PostgreSQL using the CREATE TYPE command.

Table 8-1 shows all
the built-in general-purpose data types. Most of the alternative
names listed in the "Aliases" column
are the names used internally by PostgreSQL for historical reasons. In
addition, some internally used or deprecated types are available,
but they are not listed here.

Each data type has an external representation determined by
its input and output functions. Many of the built-in types have
obvious external formats. However, several types are either
unique to PostgreSQL, such as
geometric paths, or have several possibilities for formats, such
as the date and time types. Some of the input and output
functions are not invertible. That is, the result of an output
function may lose accuracy when compared to the original
input.

The syntax of constants for the numeric types is described
in Section
4.1.2. The numeric types have a full set of corresponding
arithmetic operators and functions. Refer to Chapter 9 for more information. The
following sections describe the types in detail.

The types smallint, integer, and bigint store
whole numbers, that is, numbers without fractional
components, of various ranges. Attempts to store values
outside of the allowed range will result in an error.

The type integer is the usual
choice, as it offers the best balance between range, storage
size, and performance. The smallint
type is generally only used if disk space is at a premium.
The bigint type should only be used if
the integer range is not sufficient,
because the latter is definitely faster.

The bigint type may not function
correctly on all platforms, since it relies on compiler
support for eight-byte integers. On a machine without such
support, bigint acts the same as
integer (but still takes up eight bytes
of storage). However, we are not aware of any reasonable
platform where this is actually the case.

SQL only specifies the
integer types integer (or int) and smallint. The type
bigint, and the type names int2, int4, and int8 are extensions, which are shared with
various other SQL database
systems.

The type numeric can store numbers
with up to 1000 digits of precision and perform calculations
exactly. It is especially recommended for storing monetary
amounts and other quantities where exactness is required.
However, arithmetic on numeric values
is very slow compared to the integer types, or to the
floating-point types described in the next section.

In what follows we use these terms: The scale of a numeric is
the count of decimal digits in the fractional part, to the
right of the decimal point. The precision of a numeric
is the total count of significant digits in the whole number,
that is, the number of digits to both sides of the decimal
point. So the number 23.5141 has a precision of 6 and a scale
of 4. Integers can be considered to have a scale of zero.

Both the maximum precision and the maximum scale of a
numeric column can be configured. To
declare a column of type numeric use
the syntax

NUMERIC(precision, scale)

The precision must be positive, the scale zero or
positive. Alternatively,

NUMERIC(precision)

selects a scale of 0. Specifying

NUMERIC

without any precision or scale creates a column in which
numeric values of any precision and scale can be stored, up
to the implementation limit on precision. A column of this
kind will not coerce input values to any particular scale,
whereas numeric columns with a declared
scale will coerce input values to that scale. (The
SQL standard requires a
default scale of 0, i.e., coercion to integer precision. We
find this a bit useless. If you're concerned about
portability, always specify the precision and scale
explicitly.)

If the scale of a value to be stored is greater than the
declared scale of the column, the system will round the value
to the specified number of fractional digits. Then, if the
number of digits to the left of the decimal point exceeds the
declared precision minus the declared scale, an error is
raised.

Numeric values are physically stored without any extra
leading or trailing zeroes. Thus, the declared precision and
scale of a column are maximums, not fixed allocations. (In
this sense the numeric type is more
akin to varchar(n) than to char(n).) The
actual storage requirement is two bytes for each group of
four decimal digits, plus eight bytes overhead.

In addition to ordinary numeric values, the numeric type allows the special value NaN, meaning "not-a-number". Any operation on NaN yields another NaN. When writing this value as a constant in
a SQL command, you must put quotes around it, for example
UPDATE table SET x = 'NaN'. On
input, the string NaN is recognized
in a case-insensitive manner.

The types decimal and numeric are equivalent. Both types are part of
the SQL standard.

The data types real and double precision are inexact, variable-precision
numeric types. In practice, these types are usually
implementations of IEEE
Standard 754 for Binary Floating-Point Arithmetic (single and
double precision, respectively), to the extent that the
underlying processor, operating system, and compiler support
it.

Inexact means that some values cannot be converted exactly
to the internal format and are stored as approximations, so
that storing and printing back out a value may show slight
discrepancies. Managing these errors and how they propagate
through calculations is the subject of an entire branch of
mathematics and computer science and will not be discussed
further here, except for the following points:

If you require exact storage and calculations (such as
for monetary amounts), use the numeric type instead.

If you want to do complicated calculations with these
types for anything important, especially if you rely on
certain behavior in boundary cases (infinity, underflow),
you should evaluate the implementation carefully.

Comparing two floating-point values for equality may
or may not work as expected.

On most platforms, the real type has
a range of at least 1E-37 to 1E+37 with a precision of at
least 6 decimal digits. The double
precision type typically has a range of around 1E-307 to
1E+308 with a precision of at least 15 digits. Values that
are too large or too small will cause an error. Rounding may
take place if the precision of an input number is too high.
Numbers too close to zero that are not representable as
distinct from zero will cause an underflow error.

In addition to ordinary numeric values, the floating-point
types have several special values:

Infinity-InfinityNaN

These represent the IEEE 754
special values "infinity",
"negative infinity", and
"not-a-number", respectively. (On
a machine whose floating-point arithmetic does not follow
IEEE 754, these values will probably not work as expected.)
When writing these values as constants in a SQL command, you
must put quotes around them, for example UPDATE table SET x = 'Infinity'. On input,
these strings are recognized in a case-insensitive manner.

PostgreSQL also supports
the SQL-standard notations float and
float(p) for specifying inexact numeric
types. Here, p specifies the
minimum acceptable precision in binary digits. PostgreSQL accepts float(1) to float(24) as
selecting the real type, while
float(25) to float(53) select double
precision. Values of p
outside the allowed range draw an error. float with no precision specified is taken to
mean double precision.

Note: Prior to PostgreSQL 7.4, the precision in
float(p) was taken to mean so many
decimal digits. This has been corrected to match the SQL
standard, which specifies that the precision is measured
in binary digits. The assumption that real and double
precision have exactly 24 and 53 bits in the
mantissa respectively is correct for IEEE-standard
floating point implementations. On non-IEEE platforms it
may be off a little, but for simplicity the same ranges
of p are used on all
platforms.

The data types serial and bigserial are not true types, but merely a
notational convenience for setting up unique identifier
columns (similar to the AUTO_INCREMENT property supported by some
other databases). In the current implementation,
specifying

Thus, we have created an integer column and arranged for
its default values to be assigned from a sequence generator.
A NOT NULL constraint is applied to
ensure that a null value cannot be explicitly inserted,
either. In most cases you would also want to attach a
UNIQUE or PRIMARY KEY constraint to prevent duplicate
values from being inserted by accident, but this is not
automatic.

Note: Prior to PostgreSQL 7.3, serial implied UNIQUE. This is no longer automatic. If
you wish a serial column to be in a unique constraint or
a primary key, it must now be specified, same as with any
other data type.

To insert the next value of the sequence into the
serial column, specify that the
serial column should be assigned its
default value. This can be done either by excluding the
column from the list of columns in the INSERT statement, or through the use of the
DEFAULT key word.

The type names serial and serial4 are equivalent: both create integer columns. The type names bigserial and serial8 work
just the same way, except that they create a bigint column. bigserial
should be used if you anticipate the use of more than
231 identifiers over the lifetime of the
table.

The sequence created for a serial
column is automatically dropped when the owning column is
dropped, and cannot be dropped otherwise. (This was not true
in PostgreSQL releases
before 7.3. Note that this automatic drop linkage will not
occur for a sequence created by reloading a dump from a
pre-7.3 database; the dump file does not contain the
information needed to establish the dependency link.)
Furthermore, this dependency between sequence and column is
made only for the serial column itself.
If any other columns reference the sequence (perhaps by
manually calling the nextval
function), they will be broken if the sequence is removed.
Using a serial column's sequence in
such a fashion is considered bad form; if you wish to feed
several columns from the same sequence generator, create the
sequence as an independent object.